Cognitive Bias in Ambiguity Judgements:Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans

Positive and negative moods can be treated as prior expectations over future delivery of rewards and punishments. This provides an inferential foundation for the cognitive (judgement) bias task, now widely-used for assessing affective states in non-human animals. In the task, information about affect is extracted from the optimistic or pessimistic manner in which participants resolve ambiguities in sensory input. Here, we report a novel variant of the task aimed at dissecting the effects of affect manipulations on perceptual and value computations for decision-making under ambiguity in humans. Participants were instructed to judge which way a Gabor patch (250ms presentation) was leaning. If the stimulus leant one way (e.g. left), pressing the REWard key yielded a monetary WIN whilst pressing the SAFE key failed to acquire the WIN. If it leant the other way (e.g. right), pressing the SAFE key avoided a LOSS whilst pressing the REWard key incurred the LOSS. The size (0-100 UK pence) of the offered WIN and threatened LOSS, and the ambiguity of the stimulus (vertical being completely ambiguous) were varied on a trial-by-trial basis, allowing us to investigate how decisions were affected by differing combinations of these factors. Half the subjects performed the task in a 'Pleasantly' decorated room and were given a gift (bag of sweets) prior to starting, whilst the other half were in a bare 'Unpleasant' room and were not given anything. Although these treatments had little effect on self-reported mood, they did lead to differences in decision-making. All subjects were risk averse under ambiguity, consistent with the notion of loss aversion. Analysis using a Bayesian decision model indicated that Unpleasant Room subjects were ('pessimistically') biased towards choosing the SAFE key under ambiguity, but also weighed WINS more heavily than LOSSes compared to Pleasant Room subjects. These apparently contradictory findings may be explained by the influence of affect on different processes underlying decision-making, and the task presented here offers opportunities for further dissecting such processes

Cognitive Bias in Ambiguity Judgements: Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans

<div><p>Positive and negative moods can be treated as prior expectations over future delivery of rewards and punishments. This provides an inferential foundation for the cognitive (judgement) bias task, now widely-used for assessing affective states in non-human animals. In the task, information about affect is extracted from the optimistic or pessimistic manner in which participants resolve ambiguities in sensory input. Here, we report a novel variant of the task aimed at dissecting the effects of affect manipulations on perceptual and value computations for decision-making under ambiguity in humans. Participants were instructed to judge which way a Gabor patch (250ms presentation) was leaning. If the stimulus leant one way (e.g. left), pressing the REWard key yielded a monetary WIN whilst pressing the SAFE key failed to acquire the WIN. If it leant the other way (e.g. right), pressing the SAFE key avoided a LOSS whilst pressing the REWard key incurred the LOSS. The size (0–100 UK pence) of the offered WIN and threatened LOSS, and the ambiguity of the stimulus (vertical being completely ambiguous) were varied on a trial-by-trial basis, allowing us to investigate how decisions were affected by differing combinations of these factors. Half the subjects performed the task in a ‘Pleasantly’ decorated room and were given a gift (bag of sweets) prior to starting, whilst the other half were in a bare ‘Unpleasant’ room and were not given anything. Although these treatments had little effect on self-reported mood, they did lead to differences in decision-making. All subjects were risk averse under ambiguity, consistent with the notion of loss aversion. Analysis using a Bayesian decision model indicated that Unpleasant Room subjects were (‘pessimistically’) biased towards choosing the SAFE key under ambiguity, but also weighed WINS more heavily than LOSSes compared to Pleasant Room subjects. These apparently contradictory findings may be explained by the influence of affect on different processes underlying decision-making, and the task presented here offers opportunities for further dissecting such processes.</p></div

Schematic of the experimental procedure.

<p>The order in which affect questionnaires and the different phases of task training and testing were carried out is shown, together with basic information on the trial structure of each session. Phases within the red box took place in either the Pleasant or Unpleasant room. See text for more details.</p

Model results for Session 1.

<p><b>A</b>. Model simulation results with the estimated parameters <b><i>m</i></b>_<i>i</i>’s (Session 1). The model captures the asymmetry that is seen in the data (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165840#pone.0165840.g003" target="_blank">Fig 3</a>). The model was simulated in the same conditions (Session 1) as the actual experiment for each participant for 500 repetitions. The mean and the standard error of the mean of each participant group is shown. <b>B</b>. Model estimate with the estimated group means .</p

Estimated parameters of the four parameter model in Session 3.

<p><b>A</b>. The noise parameter <i>σ</i>. The means of Pleasant and Unpleasant room participants were not significantly different. <b>B</b>. The relative weight of losses to wins. The means of Pleasant and Unpleasant room participants were significantly different. <b>C</b>. The bias parameter <i>δ</i>. The means of Pleasant and Unpleasant room participants were significantly different. <b>D</b>. The lapse rate. The means of Pleasant and Unpleasant room participants were mildly significantly different. The parameters are shown in the inference space.</p

iBIC scores for separated groups and one group.

<p>iBIC scores for separated groups and one group.</p

Model-agnostic results (Session 3).

<p><b>A</b>. The probability of choosing the REW (risky) key in Session 3. The red (blue) line indicates the mean and the SEM of participants in the Unpleasant (Pleasant) condition. <b>B</b>. Reaction time (RT). Note that this session has 250 different stimulus ambiguities for each participants. We binned conditions with the width of <i>s</i> = 0.2.</p

Correlations between estimated parameters and the changes in questionnaire scores (affective measures).

<p><b>A</b> The Pleasant room participants. <b>B</b>. The Unpleasant room participants. The colour shows the correlation coefficient <i>r</i>, where blue means negative (<i>r</i> < 0) and red means positive (<i>r</i> > 0). The changes in score were computed as the score after Session 3 minus the score before Session 1. None of the correlations was significant after the multiple comparison correction.</p

iBIC scores for different models.

<p>iBIC scores for different models.</p

The judgement bias task.

<p>Each trial started with presentation of the trial-specific offered win (indicated by the green bar) and threatened loss (red bar). Subjects studied this information and then pressed the ‘Enter’ key. A Gabor patch stimulus was then flashed on the screen for 250ms. The subject had to decide which way the patch was leaning and press either the REW or SAFE key as appropriate. The next trial then followed. See text for details.</p